THE IMPORTANT UNANSWERED QUESTIONS IN MACHINE LEARNING (ML)

THE IMPORTANT UNANSWERED QUESTIONS IN MACHINE LEARNING (ML)

The year 2018 was expected to be the one where companies made revolutionary strides in the area of

Published By - Jason Hoffman

Machine learning is an application of artificial intelligence (AI) that enables systems to automatically learn and upgrade from experience utterly independent of any specific programme. It generally aims at the development of computer programs that can access data and use it in future to learn for themselves.

The year 2018 was expected to be the one where companies made revolutionary strides in the area of artificial intelligence (AI) and machine learning (ML. But did this happen till now?

The answer is still ‘No‘ as machine learning (ML) is much easier talked about than executed for many of us. Some questions still need to be resolved before most businesses can get to a stage where they’ve incorporated AI into their business in a real.

So, Companies that aspire to get ahead will have to work on handling these queries first, to have a better execution of machine learning.

Machine Learning Questions and Answers
I have mentioned the hot topics in ML which are yet to be studied and need answers. By giving an insight to the trends, let’s have a glance at these Machine Learning questions.

Question- Difference between Data Project and Data Science Project?
Answer– The first thing that strikes us is – Are these two terms interchangeable? These are two terms that are thrown around in development quite often, and they’re not exactly an interchangeable idea. Data Projects are merely focused on making better insights and prediction to take better decision. Whereas Data Science projects ensure that there’s no need for a natural collaboration between data analysts and data scientists and the only way to stay focused is with new predictive models that can handle all of the personal info in a data project.
Data Science projects are advanced Data Projects with advanced data from non-traditional sources.

Question – Which types of data scientists required for the development of AI systems?
Answer – Data scientists have many different strengths, and it’s often difficult for individuals in enterprises to start addressing the various problems associated with the projects that they are working.
80% of the data scientists working worldwide are currently working with big companies like Google, Facebook projects.

Question – Why are some of these data scientists leaving their jobs?
Answer- Data scientists are immensely in demand, and as a result of this demand, it is difficult to keep them in one place for long. If a company is not willing to develop and then use new computer learning initiatives, there is a good chance the data scientists will move onto a new job prospect in the future.

Question – Is there a need for collaboration throughout data science work?
Answer – A collaborative model in the workplace can help to improve effectiveness and efficiency in the future.
Collaboration in the workplace at lease with data can work much better when the bulk of analysis is split between several data scientists.

Key Takeaway – Keep some of these top questions in mind if you are exploring new AI and Machine Learning initiatives with your Business. These are some unanswered questions that we have today that are essential in the development of AI as a whole. I hope this sounds helpful if you want to be AI developed this year.

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